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共 11753 条记录，以下是271-280 订阅

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Tri-Perspective View for vision-Based 3D Semantic Occupancy Prediction

Tri-Perspective View for Vision-Based 3D Semantic Occupancy ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Huang, Yuanhui Zheng, Wenzhao Zhang, Yunpeng Zhou, Jie Lu, Jiwen Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9798350301298

Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a tri-perspective view (TPV) representation which accompanies BEV with two additional perpendicular planes. We model each point in the 3D space by summing its projected features on the three planes. To lift image features to the 3D TPV space, we further propose a transformer-based TPV encoder (TPVFormer) to obtain the TPV features effectively. We employ the attention mechanism to aggregate the image features corresponding to each query in each TPV plane. Experiments show that our model trained with sparse supervision effectively predicts the semantic occupancy for all voxels. We demonstrate for the first time that using only camera inputs can achieve comparable performance with LiDAR-based methods on the LiDAR segmentation task on nuScenes. Code: https://***/wzzheng/TPVFormer.

关键词： Autonomous driving

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Recurrent vision Transformers for Object Detection with Event Cameras

Recurrent Vision Transformers for Object Detection with Even...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

We present Recurrent vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: first, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (< 12 ms on a T4 GPU) and favorable parameter efficiency (5x fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision. Code: https://***/uzh-rpg/RVT

关键词： detection recognition: Categorization retrieval

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BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation

BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly ...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cheng, Tianheng Wang, Xinggang Chen, Shaoyu Zhang, Qian Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Peoples R China Horizon Robot Beijing Peoples R China

ISBN: (纸本)9798350301298

Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes. While, we find that box-supervised methods can produce some fine segmentation masks and we wonder whether the detectors could learn from these fine masks while ignoring low-quality masks. To answer this question, we present BoxTeacher, an efficient and end-to-end training framework for high-performance weakly supervised instance segmentation, which leverages a sophisticated teacher to generate high-quality masks as pseudo labels. Considering the massive noisy masks hurt the training, we present a mask-aware confidence score to estimate the quality of pseudo masks, and propose the noise-aware pixel loss and noise-reduced affinity loss to adaptively optimize the student with pseudo masks. Extensive experiments can demonstrate effectiveness of the proposed BoxTeacher. Without bells and whistles, BoxTeacher remarkably achieves 35.0 mask AP and 36.5 mask AP with ResNet-50 and ResNet-101 respectively on the challenging COCO dataset, which outperforms the previous state-of-the-art methods by a significant margin and bridges the gap between box-supervised and mask-supervised methods.

关键词： detection recognition: Categorization retrieval

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LANA: A Language-Capable Navigator for Instruction Following and Generation

LANA: A Language-Capable Navigator for Instruction Following...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Xiaohan Wang, Wenguan Shao, Jiayi Yang, Yi Zhejiang Univ CCAI Hangzhou Peoples R China

ISBN: (纸本)9798350301298

Recently, visual-language navigation (VLN) - entailing robot agents to follow navigation instructions - has shown great advance. However, existing literature put most emphasis on interpreting instructions into actions, only delivering "dumb" wayfinding agents. In this article, we devise LANA, a language-capable navigation agent which is able to not only execute human-written navigation commands, but also provide route descriptions to humans. This is achieved by simultaneously learning instruction following and generation with only one single model. More specifically, two encoders, respectively for route and language encoding, are built and shared by two decoders, respectively for action prediction and instruction generation, so as to exploit cross-task knowledge and capture task-specific characteristics. Throughout pretraining and fine-tuning, both instruction following and generation are set as optimization objectives. We empirically verify that, compared with recent advanced task-specific solutions, LANA attains better performances on both instruction following and route description, with nearly half complexity. In addition, endowed with language generation capability, LANA can explain to human its behaviours and assist human's wayfinding. This work is expected to foster future efforts towards building more trustworthy and socially intelligent navigation robots.

关键词： Embodied vision: Active agents simulation

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Efficient On-device Training via Gradient Filtering

Efficient On-device Training via Gradient Filtering

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Yang, Yuedong Li, Guihong Marculescu, Radu Univ Texas Austin Austin TX 78712 USA

ISBN: (纸本)9798350301298

Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-propagation algorithm. Consequently, in this paper, we propose a new gradient filtering approach which enables on-device CNN model training. More precisely, our approach creates a special structure with fewer unique elements in the gradient map, thus significantly reducing the computational complexity and memory consumption of back propagation during training. Extensive experiments on image classification and semantic segmentation with multiple CNN models (e.g., MobileNet, DeepLabV3, UPerNet) and devices (e.g., Raspberry Pi and Jetson Nano) demonstrate the effectiveness and wide applicability of our approach. For example, compared to SOTA, we achieve up to 19x speedup and 77.1% memory savings on ImageNet classification with only 0.1% accuracy loss. Finally, our method is easy to implement and deploy;over 20x speedup and 90% energy savings have been observed compared to highly optimized baselines in MKLDNN and CUDNN on NVIDIA Jetson Nano. Consequently, our approach opens up a new direction of research with a huge potential for on-device training.

关键词： Efficient and scalable vision

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Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering

Discovering the Real Association: Multimodal Causal Reasonin...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zang, Chuanqi Wang, Hanqing Pei, Mingtao Liang, Wei Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol Yangtze Delta Reg Acad Jiaxing Peoples R China

ISBN: (纸本)9798350301298

Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on statistical evidence causes, which ignores potential bias in the multimodal data. In our work, we investigate relational structure from a causal representation perspective on multimodal data and propose a novel inference framework. For visual data, question-irrelevant objects may establish simple matching associations with the answer. For textual data, the model prefers the local phrase semantics which may deviate from the global semantics in long sentences. Therefore, to enhance the generalization of the model, we discover the real association by explicitly capturing visual features that are causally related to the question semantics and weakening the impact of local language semantics on question answering. The experimental results on two large causal VideoQA datasets verify that our proposed framework 1) improves the accuracy of the existing VideoQA backbone, 2) demonstrates robustness on complex scenes and questions. The code will be released at https://***/Chuanqi-Zang/Discovering-the-Real-Association.

关键词： language reasoning vision

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Video Dehazing via a Multi-Range Temporal Alignment Network with Physical Prior

Video Dehazing via a Multi-Range Temporal Alignment Network ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Xu, Jiaqi Hu, Xiaowei Zhu, Lei Dou, Qi Dai, Jifeng Qiao, Yu Heng, Pheng-Ann Chinese Univ Hong Kong Hong Kong Peoples R China Shanghai Artificial Intelligence Lab Shanghai Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

Video dehazing aims to recover haze-free frames with high visibility and contrast. This paper presents a novel framework to effectively explore the physical haze priors and aggregate temporal information. Specifically, we design a memory-based physical prior guidance module to encode the prior-related features into long-range memory. Besides, we formulate a multi-range scene radiance recovery module to capture space-time dependencies in multiple space-time ranges, which helps to effectively aggregate temporal information from adjacent frames. Moreover, we construct the first large-scale outdoor video dehazing benchmark dataset, which contains videos in various real-world scenarios. Experimental results on both synthetic and real conditions show the superiority of our proposed method.

关键词： Low-level vision

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Towards Efficient Use of Multi-Scale Features in Transformer-Based Object Detectors

Towards Efficient Use of Multi-Scale Features in Transformer...

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Gongjie Luo, Zhipeng Tian, Zichen Zhang, Jingyi Zhang, Xiaoqin Lu, Shijian Nanyang Technol Univ S Lab Singapore Singapore Black Sesame Technol Singapore Singapore SenseTime Res Hong Kong Peoples R China Wenzhou Univ Wenzhou Peoples R China

ISBN: (纸本)9798350301298

Multi-scale features have been proven highly effective for object detection but often come with huge and even prohibitive extra computation costs, especially for the recent Transformer-based detectors. In this paper, we propose Iterative Multi-scale Feature Aggregation (IMFA) - a generic paradigm that enables efficient use of multi-scale features in Transformer-based object detectors. The core idea is to exploit sparse multi-scale features from just a few crucial locations, and it is achieved with two novel designs. First, IMFA rearranges the Transformer encoder-decoder pipeline so that the encoded features can be iteratively updated based on the detection predictions. Second, IMFA sparsely samples scale-adaptive features for refined detection from just a few keypoint locations under the guidance of prior detection predictions. As a result, the sampled multi-scale features are sparse yet still highly beneficial for object detection. Extensive experiments show that the proposed IMFA boosts the performance of multiple Transformer-based object detectors significantly yet with only slight computational overhead.

关键词： detection recognition: Categorization retrieval

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Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction

Causally-Aware Intraoperative Imputation for Overall Surviva...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Li, Xiang Qian, Xuelin Liang, Litian Kong, Lingjie Dong, Qiaole Chen, Jiejun Liu, Dingxia Yao, Xiuzhong Fu, Yanwei Fudan Univ Sch Data Sci Shanghai Peoples R China Fudan Univ Zhongshan Hosp Dept Radiol Shanghai Peoples R China

ISBN: (纸本)9798350301298

Previous efforts in vision community are mostly made on learning good representations from visual patterns. Beyond this, this paper emphasizes the high-level ability of causal reasoning. We thus present a case study of solving the challenging task of Overall Survival (OS) time in primary liver cancers. Critically, the prediction of OS time at the early stage remains challenging, due to the unobvious image patterns of reflecting the OS. To this end, we propose a causal inference system by leveraging the intraoperative attributes and the correlation among them, as an intermediate supervision to bridge the gap between the images and the final OS. Particularly, we build a causal graph, and train the images to estimate the intraoperative attributes for final OS prediction. We present a novel Causally-aware Intraoperative Imputation Model (CAWIM) that can sequentially predict each attribute using its parent nodes in the estimated causal graph. To determine the causal directions, we propose a splitting-voting mechanism, which votes for the direction for each pair of adjacent nodes among multiple predictions obtained via causal discovery from heterogeneity. The practicability and effectiveness of our method are demonstrated by the promising results on liver cancer dataset of 361 patients with long-term observations.

关键词： cell microscopy Medical and biological vision

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Slimmable Dataset Condensation

Slimmable Dataset Condensation

引用

ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Songhua Ye, Jingwen Yu, Runpeng Wang, Xinchao Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798350301298

Dataset distillation, also known as dataset condensation, aims to compress a large dataset into a compact synthetic one. Existing methods perform dataset condensation by assuming a fixed storage or transmission budget. When the budget changes, however, they have to repeat the synthesizing process with access to original datasets, which is highly cumbersome if not infeasible at all. In this paper, we explore the problem of slimmable dataset condensation, to extract a smaller synthetic dataset given only previous condensation results. We first study the limitations of existing dataset condensation algorithms on such a successive compression setting and identify two key factors: (1) the inconsistency of neural networks over different compression times and (2) the underdetermined solution space for synthetic data. Accordingly, we propose a novel training objective for slimmable dataset condensation to explicitly account for both factors. Moreover, synthetic datasets in our method adopt a significance-aware parameterization. Theoretical derivation indicates that an upper-bounded error can be achieved by discarding the minor components without training. Alternatively, if training is allowed, this strategy can serve as a strong initialization that enables a fast convergence. Extensive comparisons and ablations demonstrate the superiority of the proposed solution over existing methods on multiple benchmarks.

关键词： Efficient and scalable vision

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